For many processes and activities, it would be useful to be able to efficiently monitor, predict or control the status of the organisms taking part in these processes or activities. This applies particularly to processes involving physical activity or where metabolic energy is produced by the body, and thus to organisms capable of physical activity or producing metabolic energy such as humans or animals. One way to monitor and manage bioprocesses of living organisms is through systems biology. To gain more insight in the functioning of biological systems and the responses of the living organisms to those systems, purely experimental deduction will not suffice because of the intrinsic complexity of biological systems (Kitano, 2002). However, the combination of engineering system identification theory and experimental biology—called systems biology—offers great perspectives. Systems biology advocates decompositions of complex biological systems in several subsystems according to traditional engineering (e.g. McAdams et. al, 1995; Hartwell et. al, 1999) and engineering control theory (e.g. Csete et. al, 2002).
The basic idea behind systems biology is to identify a mathematical model of the system and then use this model to control or to design a controller so that the behaviour of the system will follow a desired profile (e.g. Tomlin et. al, 2005). The most commonly used control processes are feedback connections. However the essential part of an efficient control is the prediction of how the so-called considered process output will dynamically respond to the variation of a system input. Consider for example a driver who is driving a car (see FIG. 1). A possible process output is the driving direction of the car and another possible process output is the position of the steering wheel. First the driver will at every moment use the principle of continuous feedback by visually comparing the actual driving direction with the desired one and use the difference to control the position of the steering wheel. The feedback however is a necessary but not a sufficient condition for efficient process control. If feedback would be enough for an efficient control, then everybody could drive a car, or sail a boat, or skate a skateboard which obviously is not the case. An efficient control needs at every moment a good prediction of how the process output will dynamically respond to a variation of the process input. This is what a driver is doing when driving a car, this is why a skater can skate, as he knows how the skateboard will respond to a variation of the control input. If an engineer would design an autopilot function for driving a car (FIG. 1) than he first would design a mathematical model of the vehicle dynamics, which describes how the driving direction of the car (output) changes when turning the steering wheel (input). By using sensors to measure the output continuously, the engineer uses the model to calculate how these measurements should be employed to adjust the inputs automatically, so that if the car does not follow the desired driving direction, it is quickly and smoothly guided back to the desired driving track (after Berckmans and De Moor, 1996).
Thus, by applying the modern control theory with model based control techniques, (bio)processes can be monitored and controlled (Golten and Verwer, 1991; Camacho and Bordons, 1999). This requires at every moment the availability of a process model that allows predicting the dynamic response of the process output to a variation of one or more process inputs.
Many different models have been described in the art. A first category are mechanistic models. These describe the dynamic response based on physical, physiological and biochemical laws, resulting in complex models, consisting of many equations and model parameters. Such models are suitable for gaining insight, transfer of scientific knowledge and for simulation of processes, but a practical drawback is that they are too complex and too inaccurate for control purposes in practice.
Beside mechanistic models, also empirical (non-linear) models are found in literature. These models are mainly the result of a non-linear regression analysis applied to data from a human or animal. The advantages of such models are that they are accurate and have not such a complex structure. However, the models are estimated off-line (after all the data are gathered). Since these models are not updated in real time and not all relevant process inputs or disturbing factors are taken into account, these known models are also less useful for monitoring or control purposes.
Ideally, monitoring and controlling bio-response of living organisms should be done by using less complex models and modelling techniques to model the dynamic response of a bioprocess output to a variation of one or more process inputs, or to a variation in one or more disturbing variables that influence the process outputs. For application of the modelling approach to living organisms such a process model should be simple and compact so that it can be applied in real time to be accurate. To be applicable on living organisms, such a model should overcome three problems often encountered in the prior art.
A first important consideration that needs to be made is that a living organism is a very complex system. Today it is for example not possible to write down all dynamic biochemical and biological processes that occur in a single cell of a human, animal or plant. E.g. the process of infection is not analyzed or understood to that level since it is so complex.
Secondly, a living organism (be it human, animal or plant) is not responding or behaving like the average of a population. The subjects to be monitored have thus far been regarded as the average of a population, but not as an individual living organism. For instance, the training of athletes or the handling of animals is done by considering the living organism as the average of a population. Existing equipment (e.g. commercially available heart rate monitors) is based on statistical relationships taken from a population of many individuals, the amount and concentration of medication in the whole health care systems is not individualized, research about sports training is based on statistical curves measured on many different athletes. In reality however no living organism is acting or behaving as an average of a population, but instead as an individual.
Each living organism is individually different. Unlike mechanical objects that can be produced in a very accurate and identical way, individual living organisms differ in many ways from each other and also behave in an individual way.
Thirdly, an individual living organism is not always responding to process inputs or environmental variables (physical environment, stress, medication, food, etc.) in an identical way. Thus, all biological responses or behaviour in a same individual can be time varying. The way individuals fall asleep for example can differ from one time to another. One moment, they are concentrated and the next moment this might be totally different. Living organisms are not responding dynamically in a standard way: they are time-varying systems. A living organism is responding in a dynamic way (i.e. biological responses or bioresponses) to variations of most environmental variables but can show spontaneous biological behaviour as well. Living organisms also are dynamic systems: they respond and behave in a dynamic way.
Looking to these higher mentioned most important characteristics in relation to the monitoring and controlling of the status of living organisms it can be stated that living organisms are Complex, Individual, Time varying and Dynamic (“CITD”) systems.
The final problem relates to the monitoring and controlling of these CITD systems. Most existing monitoring techniques or controlling tools are not developed for CITD systems. For instance, a calibration curve of a sensor assumes that the individual differences of the sensors will be small enough to guarantee a high accuracy. However, for living organisms (i.e. CITD systems) this does not work since none of them is acting as the theoretical average of a population. One approach to circumvent this problem is by taking many more samples, like in neural networks for classification. However, for CITD systems this means that the solution never can be more accurate than the standard deviation around the theoretical average and for living organisms this ends up in a high error for each individual at a given moment.
This problem has been solved previously, as described in European patent EP1392109. A method is described to monitor and control individual living organisms as CITD systems. The mathematical model underlying this method is a dynamic and adaptive data-based on-line modelling technique, which manages to model accurately the CITD systems using only a limited number of parameters. Although the exemplified embodiments mainly relate to monitoring biomass production in different animals, the methods are applicable in many other situations. Indeed, it is made clear that any bioresponse can be monitored based on the appropriate real-time information on bioprocess inputs and outputs. This applies to humans, animals, as well as plants.
Although the method proposed in EP1392109 can be used to adequately monitor and control almost all bioprocesses, there are some embodiments where this method can be made more efficient. This specifically applies to circumstances in which the relationship between bioprocess inputs and outputs involves factors other than those directly related to mechanical activity or basal metabolism, e.g. because of the involvement of mental or emotional processes, arousal, stress, fear or the like. Up till now, these components could not be adequately described and thus not be used as an input or output to the model.
Nevertheless, it is well recognized that there is a link between the mental/emotional/psychological/psychophysiological/cognitive or general arousal status of an individual (human or animal) and performance of a task.
The classic description of the relationship between arousal and performance is the Yerkes-Dodson law (Yerkes and Dodson, 1908). This empirically based law, which was originally demonstrated using mice, dictates that performance increases with cognitive arousal, but only to a certain point: when levels of arousal become too high, performance will decrease. A corollary is that there is an optimal level of arousal for a given task. The process is often demonstrated graphically as an inverted U-shaped curve (FIG. 2), increasing and then decreasing with higher levels of arousal. It has been proposed that different tasks may require different levels of arousal. For example, difficult or intellectually demanding tasks may require a lower level of arousal for optimal performance (to facilitate concentration), whereas tasks demanding stamina or persistence may be performed better with higher levels of arousal (to increase motivation). The effect of the difficulty of tasks later on led to the hypothesis that the Yerkes-Dodson Law can be decomposed into two distinct factors. The upward part of the converted U can be thought of as the energizing effect of arousal. The downward part on the other hand is caused by negative effects of arousal (or stress) on cognitive processes, like attention (“tunnel vision”), memory, and problem-solving.
This principle is central to the science of psychophysiology. Psychophysiology studies interactions between the mind and body by recording how the body is functioning and relating the functions recorded to behaviour. Changes in the body's functioning cause changes in behaviour and vice versa. Psychophysiological recording techniques are generally non-invasive. That is, they record from the body's surface and nothing goes into the person being recorded. Psychophysiological recordings are frequently used to help assess problems with how the body is functioning.
Psychophysiology is the science of understanding the link between psychology and physiology. Psychophysiology is different from physiological psychology in that psychophysiology looks at the way psychological activities produce physiological responses, while physiological psychology looks at the physiological mechanisms which lead to psychological activity. Historically, most psychophysiologists tended to examine the physiological responses and organ systems innervated by the autonomic nervous system. More recently, psychophysiologists have been equally, or potentially more, interested in the central nervous system, exploring cortical brain potentials such as the many types of event related potentials (ERPs), brain waves, functional neuroimagery (fMRI), PET, MEG, etc.
A psychophysiologist may look at how exposure to a stressful or physiological arousing situation will produce a result in physiological variables such as the cardiovascular system (a change in heart rate (HR), vasodilation/vasoconstriction, myocardial contractility, or stroke volume). To control these psychophysiological events, biofeedback is often used.
With biofeedback is meant providing real time information from psychophysiological recordings about the levels at which physiological systems are functioning. Biofeedback does not need to involve the use of computers, electronic devices etc. For example, a mirror is a perfectly good biofeedback device for many aspects of gait retraining. Electronic biofeedback devices are designed to record physiological functions non-invasively. Most record from the surface of the skin. The information recorded by surface sensors is frequently sent to a computer for processing and then displayed on the monitor and/or through speakers. The individual being recorded and any therapist or coach who may be present can attend to the display of information and incorporate it into what ever process they are attempting to perform.
The basic principles of biofeedback have been demonstrated while doing animal experimentation conditioning the behaviour of rats. It was found that, by stimulating the pleasure centre of a rat's brain with electricity; it was possible to train them to control phenomena ranging from their heart rates to their brainwaves. Until that point, it was believed that bodily processes such as heart rate were under the control of the autonomic nervous system and not responsive to conscious effort.
The phenomenon of biofeedback is believed to work as follows: stressful or physiological arousing events produce strong emotions or mental processes, which in turn lead to certain physiological responses. Many of these responses are controlled by the sympathetic nervous system, the network of nerve tissues that helps prepare the body to meet emergencies by preparing the typical “flight or fight” response.
The typical pattern of response to emergencies probably emerged during the time when all humans faced mostly physical threats. Although the “threats” we now live with are seldom physical, the body reacts as if they were: The pupils dilate to let in more light. Sweat pours out, reducing the chance of skin cuts. Blood vessels near the skin contract to reduce bleeding, while those in the brain and muscles dilate to increase the oxygen supply. The gastrointestinal tract, including the stomach and intestines, slows down to reduce the energy expensed in digestion. The heart beats faster, and blood pressure rises. Normally, people calm down when a stressful or physiological arousing event is over especially if they have done something to cope with it. For instance, when someone is walking down a dark street and hears someone running towards him, he typically will get aroused, i.e. his body will prepare him to ward off an attacker or run fast enough to get away. When the potentially threatening situation is over, he gradually relaxes.
If someone gets angry at his boss, it's a different matter. His body may prepare to fight. But in order not to lose his job, he will try to ignore the angry feelings. Similarly, if an individual gets stalled in traffic, there's nothing he can do to get away. These situations can literally make someone sick. Their body has prepared for action, but they cannot act. Individuals differ in the way they respond to stress or arousal. In some, one function, such as blood pressure, becomes more active while others remain normal. Many experts believe that these individual physical responses to stress or arousal can become habitual. When the body is repeatedly aroused, one or more functions may become permanently overactive. Actual damage to bodily tissues may eventually result.
Biofeedback is often aimed at changing habitual reactions to stress that can cause pain or disease. Many clinicians believe that some of their patients and clients have forgotten how to relax. Feedback of physical responses such as skin temperature and muscle tension provides information to help patients recognize a relaxed state. The feedback signal may also act as a kind of reward for reducing tension.
The value of a feedback signal as information and reward may be even greater in the treatment of patients with paralyzed or spastic muscles. With these patients, biofeedback seems to be primarily a form of skill training like learning to pitch a ball. Instead of watching the ball, the patient watches the machine, which monitors activity in the affected muscle. Stroke victims with paralyzed arms and legs, for example, see that some part of their affected limbs remains active. The signal from the biofeedback machine proves it. This signal can guide the exercises that help patients regain use of their limbs. Perhaps just as important, the feedback convinces patients that the limbs are still alive. This reassurance often encourages them to continue their efforts.
Clinical biofeedback techniques that grew out of the early laboratory procedures are now widely used to treat an ever-lengthening list of conditions. These include amongst others migraine, headaches, tension headaches, and many other types of pain, disorders of the digestive system, high blood pressure and its opposite, low blood pressure, irregular heartbeats or cardiac arrhythmias (abnormalities, sometimes dangerous, in the rhythm of the heartbeat), Raynaud's disease (a circulatory disorder that causes uncomfortably cold hands), epilepsy, paralysis and other movement disorders, asthma, irritable bowel syndrome, hot flashes, nausea and vomiting associated with chemotherapy, and incontinence. Biofeedback is also used to improve performance under physiologically arousing conditions, e.g. for training pilots (Cowings et al., 2001).
From the Yerkes-Dodson law, it follows that it would be beneficial to monitor and control arousal to facilitate or enhance task performance. Multiple variables indicative of arousal are known in the art (e.g. EEG, heart rate, skin conductance, . . . ). These indices of arousal however are not only influenced by arousal. Total heart rate for instance is composed of heart rate required for basal metabolism, for mechanical activity, for heat balance, as well as for arousal. In practice, arousal will be measured in two conditions: in (resting) subjects as such, and in (resting) subjects subjected to conditions increasing arousal. The differences between the two settings are then attributed to (physiological) arousal, the other components attributing to the index of arousal (such as mechanical activity, basal metabolism and heat balance) are considered invariable between the settings. However, this precludes the possibility of accurately measuring arousal in settings where changes in mechanical activity (or basal metabolism, or heat balance) will certainly have an impact on the index of arousal variable (e.g. in sports or other physical activities). Also, such studies are often based on off-line measurements (i.e. before and after induction of arousal). Moreover, there are pitfalls in relying on any single measure of arousal. For example, alerting caused by fear-evoking stimuli causes an increase in heart rate and other autonomic indices. In contrast, phasic alerting caused by orienting toward a non-threatening stimulus causes a slowing of the heart and other internal organs. Thus there is a need for methods that can specifically monitor the component of the index of arousal that is indicative of actual arousal, independent of physical activity. More particularly, such methods should take into account the complex, individual, time-varying and dynamic character of the individual organism monitored and be suitable for monitoring physical activity related variables and environmental variables as well.